'''simple docstring'''




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import SwiftFormerForImageClassification, SwiftFormerModel
     from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor

class 	_lowercase:



   """simple docstring"""





   def __init__(				self:     Any      ,a:     Union[str, Any]      ,a:     Union[str, Any]=13      ,a:     int=3      ,a:     Any=True      ,a:     int=True      ,a:     Any=0.1      ,a:     Any=0.1      ,a:     List[str]=224      ,a:     Any=1000      ,a:     Union[str, Any]=[3, 3, 6, 4]      ,a:     Optional[int]=[48, 56, 112, 220]      ,):
    __UpperCAmelCase								=       parent
    __UpperCAmelCase								=       batch_size
    __UpperCAmelCase								=       num_channels
    __UpperCAmelCase								=       is_training
    __UpperCAmelCase								=       use_labels
    __UpperCAmelCase								=       hidden_dropout_prob
    __UpperCAmelCase								=       attention_probs_dropout_prob
    __UpperCAmelCase								=       num_labels
    __UpperCAmelCase								=       image_size
    __UpperCAmelCase								=       layer_depths
    __UpperCAmelCase								=       embed_dims





   def       snake_case						(				self:     Dict						):
    __UpperCAmelCase								=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

    __UpperCAmelCase								=       None
    if self.use_labels:
     __UpperCAmelCase								=       ids_tensor([self.batch_size]      ,self.num_labels						)

    __UpperCAmelCase								=       self.get_config()

    return config, pixel_values, labels





   def       snake_case						(				self:     List[str]						):
    return SwiftFormerConfig(
        depths=self.layer_depths      ,embed_dims=self.embed_dims      ,mlp_ratio=4      ,downsamples=[True, True, True, True]      ,hidden_act='gelu'      ,num_labels=self.num_labels      ,down_patch_size=3      ,down_stride=2      ,down_pad=1      ,drop_rate=0.0      ,drop_path_rate=0.0      ,use_layer_scale=UpperCamelCase_      ,layer_scale_init_value=1e-5      ,)





   def       snake_case						(				self:     List[str]      ,a:     Optional[int]      ,a:     List[Any]      ,a:     Tuple						):
    __UpperCAmelCase								=       SwiftFormerModel(config=UpperCamelCase_						)
    model.to(UpperCamelCase_						)
    model.eval()
    __UpperCAmelCase								=       model(UpperCamelCase_						)
    self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.embed_dims[-1], 7, 7)						)





   def       snake_case						(				self:     int      ,a:     Dict      ,a:     Tuple      ,a:     str						):
    __UpperCAmelCase								=       self.num_labels
    __UpperCAmelCase								=       SwiftFormerForImageClassification(UpperCamelCase_						)
    model.to(UpperCamelCase_						)
    model.eval()
    __UpperCAmelCase								=       model(UpperCamelCase_      ,labels=UpperCamelCase_						)
    self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)						)

    __UpperCAmelCase								=       SwiftFormerForImageClassification(UpperCamelCase_						)
    model.to(UpperCamelCase_						)
    model.eval()

    __UpperCAmelCase								=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
    __UpperCAmelCase								=       model(UpperCamelCase_						)
    self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)						)





   def       snake_case						(				self:     Union[str, Any]						):
    ((__UpperCAmelCase)	,      (__UpperCAmelCase)	,      (__UpperCAmelCase))								=       self.prepare_config_and_inputs()
    __UpperCAmelCase								=       {'pixel_values': pixel_values}
    return config, inputs_dict




@require_torch
class 	_lowercase(  a_ ,a_ ,unittest.TestCase					):



   """simple docstring"""





   __lowerCamelCase     =	(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   __lowerCamelCase     =	(
       {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   __lowerCamelCase     =	False
   __lowerCamelCase     =	False
   __lowerCamelCase     =	False
   __lowerCamelCase     =	False
   __lowerCamelCase     =	False





   def       snake_case						(				self:     int						):
    __UpperCAmelCase								=       SwiftFormerModelTester(self						)
    __UpperCAmelCase								=       ConfigTester(
        self      ,config_class=UpperCamelCase_      ,has_text_modality=UpperCamelCase_      ,hidden_size=37      ,num_attention_heads=12      ,num_hidden_layers=12      ,)





   def       snake_case						(				self:     List[str]						):
    self.config_tester.run_common_tests()





   @unittest.skip(reason='SwiftFormer does not use inputs_embeds'						)
   def       snake_case						(				self:     Optional[int]						):
    pass





   def       snake_case						(				self:     Dict						):
    __UpperCAmelCase	,      __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __UpperCAmelCase								=       model_class(UpperCamelCase_						)
     __UpperCAmelCase								=       model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(UpperCamelCase_      ,nn.Linear						)						)





   def       snake_case						(				self:     Union[str, Any]						):
    __UpperCAmelCase	,      __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __UpperCAmelCase								=       model_class(UpperCamelCase_						)
     __UpperCAmelCase								=       inspect.signature(model.forward						)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __UpperCAmelCase								=       [*signature.parameters.keys()]

     __UpperCAmelCase								=       ['pixel_values']
     self.assertListEqual(arg_names[:1]      ,UpperCamelCase_						)





   def       snake_case						(				self:     str						):
    __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase_						)





   def       snake_case						(				self:     Any						):
    __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_						)





   @slow
   def       snake_case						(				self:     Union[str, Any]						):

    for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase								=       SwiftFormerModel.from_pretrained(UpperCamelCase_						)
     self.assertIsNotNone(UpperCamelCase_						)





   @unittest.skip(reason='SwiftFormer does not output attentions'						)
   def       snake_case						(				self:     Union[str, Any]						):
    pass





   def       snake_case						(				self:     Any						):

    def check_hidden_states_output(a:     List[Any]      ,a:     List[str]      ,a:     Union[str, Any]						):
     __UpperCAmelCase								=       model_class(UpperCamelCase_						)
     model.to(UpperCamelCase_						)
     model.eval()

     with torch.no_grad():
      __UpperCAmelCase								=       model(**self._prepare_for_class(UpperCamelCase_      ,UpperCamelCase_						)						)

     __UpperCAmelCase								=       outputs.hidden_states

     __UpperCAmelCase								=       8
     self.assertEqual(len(UpperCamelCase_						)      ,UpperCamelCase_						)  # TODO

     # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
     # with the width and height being successively divided by 2, after every 2 blocks
     for i in range(len(UpperCamelCase_						)						):
      self.assertEqual(
          hidden_states[i].shape      ,torch.Size(
              [
                  self.model_tester.batch_size,
                  self.model_tester.embed_dims[i // 2],
                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
              ]						)      ,)

    __UpperCAmelCase	,      __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __UpperCAmelCase								=       True
     check_hidden_states_output(UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_						)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __UpperCAmelCase								=       True

     check_hidden_states_output(UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_						)





   def       snake_case						(				self:     Union[str, Any]						):

    def _config_zero_init(a:     Optional[Any]						):
     __UpperCAmelCase								=       copy.deepcopy(UpperCamelCase_						)
     for key in configs_no_init.__dict__.keys():
      if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
       setattr(UpperCamelCase_      ,UpperCamelCase_      ,1e-10						)
      if isinstance(getattr(UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_						)      ,UpperCamelCase_						):
       __UpperCAmelCase								=       _config_zero_init(getattr(UpperCamelCase_      ,UpperCamelCase_						)						)
       setattr(UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_						)
     return configs_no_init

    __UpperCAmelCase	,      __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs_for_common()

    __UpperCAmelCase								=       _config_zero_init(UpperCamelCase_						)
    for model_class in self.all_model_classes:
     __UpperCAmelCase								=       model_class(config=UpperCamelCase_						)
     for name, param in model.named_parameters():
      if param.requires_grad:
       self.assertIn(
           ((param.data.mean() * 1e9) / 1e9).round().item()      ,[0.0, 1.0]      ,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,)





   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
   def       snake_case						(				self:     Optional[int]						):
    pass




def     __snake_case			(							):
 __UpperCAmelCase								=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
 return image

@require_torch
@require_vision
class 	_lowercase(  unittest.TestCase					):



   """simple docstring"""





   @cached_property
   def       snake_case						(				self:     int						):
    return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'						) if is_vision_available() else None





   @slow
   def       snake_case						(				self:     Any						):
    __UpperCAmelCase								=       SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'						).to(UpperCamelCase_						)

    __UpperCAmelCase								=       self.default_image_processor
    __UpperCAmelCase								=       prepare_img()
    __UpperCAmelCase								=       image_processor(images=UpperCamelCase_      ,return_tensors='pt'						).to(UpperCamelCase_						)

    # forward pass
    with torch.no_grad():
     __UpperCAmelCase								=       model(**UpperCamelCase_						)

    # verify the logits
    __UpperCAmelCase								=       torch.Size((1, 1000)						)
    self.assertEqual(outputs.logits.shape      ,UpperCamelCase_						)

    __UpperCAmelCase								=       torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]						).to(UpperCamelCase_						)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,UpperCamelCase_      ,atol=1e-4						)						)











"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


snake_case : Optional[Any]     		=		logging.get_logger(__name__)

snake_case : str     		=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case : List[Any]     		=		{
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

snake_case : Tuple     		=		{"""mobilebert-uncased""": 5_1_2}


snake_case : List[str]     		=		{}



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_INIT_CONFIGURATION
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = MobileBertTokenizer







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	Optional[int]=None				,    UpperCamelCase_ :	str=None				,    UpperCamelCase_ :	Tuple=True				,    UpperCamelCase_ :	Any="[UNK]"				,    UpperCamelCase_ :	str="[SEP]"				,    UpperCamelCase_ :	Any="[PAD]"				,    UpperCamelCase_ :	Dict="[CLS]"				,    UpperCamelCase_ :	str="[MASK]"				,    UpperCamelCase_ :	Union[str, Any]=True				,    UpperCamelCase_ :	Dict=None				,    **UpperCamelCase_ :	Dict				,    ):





           '''simple docstring'''

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    do_lower_case=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    tokenize_chinese_chars=UpperCamelCase_				,    strip_accents=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
           if (
               normalizer_state.get('lowercase'				,    UpperCamelCase_ ) != do_lower_case
               or normalizer_state.get('strip_accents'				,    UpperCamelCase_ ) != strip_accents
               or normalizer_state.get('handle_chinese_chars'				,    UpperCamelCase_ ) != tokenize_chinese_chars
           ):
                  __magic_name__      				=    getattr(UpperCamelCase_				,    normalizer_state.pop('type' ) )
                  __magic_name__      				=    do_lower_case
                  __magic_name__      				=    strip_accents
                  __magic_name__      				=    tokenize_chinese_chars
                  __magic_name__      				=    normalizer_class(**UpperCamelCase_ )

           __magic_name__      				=    do_lower_case







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	List[Any]=None ):





           '''simple docstring'''

           __magic_name__      				=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           __magic_name__      				=    self._tokenizer.model.save(UpperCamelCase_				,    name=UpperCamelCase_ )
           return tuple(UpperCamelCase_ )